SLB and NVIDIA announced an expanded partnership to design and deploy AI infrastructure specifically built for the energy industry, marking a shift from experimental AI projects to enterprise-scale deployment across oil, gas, and renewable energy operations. The collaboration will create modular data centers, domain-specific AI models, and what the companies call an "AI Factory for Energy" to help energy companies transform their operational data into actionable insights. What Is an AI Factory for Energy, and Why Do Energy Companies Need One? Energy companies generate vast amounts of operational data across subsurface exploration, production facilities, and energy infrastructure, but much of that data remains siloed and underutilized. An "AI Factory for Energy" is essentially a reference environment powered by generative AI models and industrial-scale agentic AI (AI systems that can take autonomous actions) running on SLB's digital platforms. This infrastructure allows energy companies to process their data at scale and extract insights that can improve operational efficiency and support lower-carbon energy systems. The partnership builds on a relationship between SLB and NVIDIA that began in 2008, when NVIDIA's accelerated computing first enhanced SLB's subsurface visualization and seismic imaging software. In 2024, the companies announced plans to develop generative AI solutions for the energy sector, and today's announcement represents a significant expansion of that work. How Will This AI Infrastructure Actually Work in Practice? - Modular Data Center Design: SLB will serve as the design partner for NVIDIA's DSX AI factories, using a modular approach where components are manufactured offsite and assembled on-site. This reduces costs, labor constraints, and lead times while enabling rapid and flexible scaling as demand grows. - Domain-Specific AI Models: The collaboration will combine NVIDIA's Omniverse libraries and Nemotron open models with SLB's digital and AI platforms, creating AI tools tailored to energy industry workflows rather than generic, off-the-shelf solutions. - Accelerated Computing for Large Datasets: The companies will optimize how large datasets and AI models are processed across SLB digital platforms using the latest NVIDIA AI infrastructure, aiming to establish new performance and efficiency benchmarks for energy applications. The work spans traditional machine learning, generative AI, and emerging agentic AI technologies designed to improve performance and support reliable, efficient, and lower-carbon energy systems. Why Does This Partnership Matter for Climate and Sustainability? Energy companies sit at the intersection of climate action and operational complexity. They manage massive infrastructure, make decisions that affect global energy supply, and increasingly face pressure to reduce carbon emissions. By deploying AI at scale, these companies can optimize operations in ways that were previously impossible, potentially reducing waste, improving efficiency, and supporting the transition to cleaner energy systems. "The winners in AI will be companies with the best data, the deepest domain expertise and the ability to scale," said Demos Pafitis, SLB's chief technology officer. "By collaborating with NVIDIA to advance modular data center construction and harness our domain expertise and digital platforms, we're enabling the energy industry to deploy AI at scale and transform operational data into smarter decisions." Demos Pafitis, Chief Technology Officer at SLB Vladimir Troy, vice president of AI Infrastructure at NVIDIA, emphasized the broader significance of this work: "AI is becoming the engine of a new industrial revolution, and the energy industry is at its forefront. Building AI Factory infrastructure and domain models is needed to turn massive amounts of energy data into actionable insights and accelerate more efficient and sustainable energy systems". What Does This Mean for the Energy Transition? This partnership represents a critical shift in how AI is being deployed in one of the world's most important industries. Rather than energy companies experimenting with AI in isolated projects, they now have access to purpose-built infrastructure and models designed specifically for their data and workflows. This could accelerate the pace at which energy companies optimize their operations, reduce inefficiencies, and support the broader energy transition. The modular data center approach is particularly significant because it addresses a real bottleneck in AI deployment: infrastructure. By making data centers faster and cheaper to build and scale, SLB and NVIDIA are removing one of the barriers that has slowed AI adoption in energy. Companies can now expand their AI capabilities quickly as their needs grow, rather than waiting months or years for traditional data center construction. The announcement also signals confidence from both companies that AI is moving beyond the experimental phase in energy. SLB's 100-year history in energy innovation, combined with NVIDIA's leadership in AI infrastructure, suggests this partnership is built on deep industry knowledge and technical expertise rather than hype. For energy companies watching these developments, the message is clear: AI infrastructure tailored to your industry is now available at scale.
